Text Analysis with R for Students of Literature

Author: Matthew Jockers
Publisher: Springer
ISBN: 3319031643
Format: PDF, Mobi
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Text Analysis with R for Students of Literature is written with students and scholars of literature in mind but will be applicable to other humanists and social scientists wishing to extend their methodological tool kit to include quantitative and computational approaches to the study of text. Computation provides access to information in text that we simply cannot gather using traditional qualitative methods of close reading and human synthesis. Text Analysis with R for Students of Literature provides a practical introduction to computational text analysis using the open source programming language R. R is extremely popular throughout the sciences and because of its accessibility, R is now used increasingly in other research areas. Readers begin working with text right away and each chapter works through a new technique or process such that readers gain a broad exposure to core R procedures and a basic understanding of the possibilities of computational text analysis at both the micro and macro scale. Each chapter builds on the previous as readers move from small scale “microanalysis” of single texts to large scale “macroanalysis” of text corpora, and each chapter concludes with a set of practice exercises that reinforce and expand upon the chapter lessons. The book’s focus is on making the technical palatable and making the technical useful and immediately gratifying.

Humanities Data in R

Author: Taylor Arnold
Publisher: Springer
ISBN: 3319207024
Format: PDF, Kindle
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​This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries. ​

Macroanalysis

Author: Matthew L. Jockers
Publisher: University of Illinois Press
ISBN: 025209476X
Format: PDF, Docs
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In this volume, Matthew L. Jockers introduces readers to large-scale literary computing and the revolutionary potential of macroanalysis--a new approach to the study of the literary record designed for probing the digital-textual world as it exists today, in digital form and in large quantities. Using computational analysis to retrieve key words, phrases, and linguistic patterns across thousands of texts in digital libraries, researchers can draw conclusions based on quantifiable evidence regarding how literary trends are employed over time, across periods, within regions, or within demographic groups, as well as how cultural, historical, and societal linkages may bind individual authors, texts, and genres into an aggregate literary culture. Moving beyond the limitations of literary interpretation based on the "close-reading" of individual works, Jockers describes how this new method of studying large collections of digital material can help us to better understand and contextualize the individual works within those collections.

Natural Language Processing and Text Mining

Author: Anne Kao
Publisher: Springer Science & Business Media
ISBN: 1846287545
Format: PDF, Kindle
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Natural Language Processing and Text Mining not only discusses applications of Natural Language Processing techniques to certain Text Mining tasks, but also the converse, the use of Text Mining to assist NLP. It assembles a diverse views from internationally recognized researchers and emphasizes caveats in the attempt to apply Natural Language Processing to text mining. This state-of-the-art survey is a must-have for advanced students, professionals, and researchers.

Reading Machines

Author: Stephen Ramsay
Publisher: University of Illinois Press
ISBN: 0252093445
Format: PDF, ePub
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Besides familiar and now-commonplace tasks that computers do all the time, what else are they capable of? Stephen Ramsay's intriguing study of computational text analysis examines how computers can be used as "reading machines" to open up entirely new possibilities for literary critics. Computer-based text analysis has been employed for the past several decades as a way of searching, collating, and indexing texts. Despite this, the digital revolution has not penetrated the core activity of literary studies: interpretive analysis of written texts. Computers can handle vast amounts of data, allowing for the comparison of texts in ways that were previously too overwhelming for individuals, but they may also assist in enhancing the entirely necessary role of subjectivity in critical interpretation. Reading Machines discusses the importance of this new form of text analysis conducted with the assistance of computers. Ramsay suggests that the rigidity of computation can be enlisted in the project of intuition, subjectivity, and play.

The Digital Humanities and the Digital Modern

Author: James Smithies
Publisher: Springer
ISBN: 1137499443
Format: PDF
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This book provides new critical and methodological approaches to digital humanities, intended to guide technical development as well as critical analysis. Informed by the history of technology and culture and new perspectives on modernity, Smithies grounds his claims in the engineered nature of computing devices and their complex entanglement with our communities, our scholarly traditions, and our sense of self. The distorting mentalité of the digital modern informs our attitudes to computers and computationally intensive research, leading scholars to reject articulations of meaning that admit the interdependence of humans and the complex socio-technological systems we are embedded in. By framing digital humanities with the digital modern, researchers can rebuild our relationship to technical development, and seek perspectives that unite practical and critical activity. This requires close attention to the cyber-infrastructures that inform our research, the software-intensive methods that are producing new knowledge, and the ethical issues implicit in the production of digital humanities tools and methods. The book will be of interest to anyone interested in the intersection of technology with humanities research, and the future of digital humanities.

Humanities Data in R

Author: Taylor Arnold
Publisher: Springer
ISBN: 3319207024
Format: PDF, Kindle
Download Now
​This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, and images. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose background programming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries. ​

Writing Literature Reviews

Author: Jose L. Galvan
Publisher: Taylor & Francis
ISBN: 1351858920
Format: PDF, Mobi
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This useful guide educates students in the preparation of literature reviews for term projects, theses, and dissertations. The authors provide numerous examples from published reviews that illustrate the guidelines discussed throughout the book. ? New to the seventh edition: ? Each chapter breaks down the larger holistic review of literature exercise into a series of smaller, manageable steps Practical instructions for navigating today’s digital libraries Comprehensive discussions about digital tools, including bibliographic and plagiarism detection software Chapter activities that reflect the book’s updated content New model literature reviews Online resources designed to help instructors plan and teach their courses (www.routledge.com/9780415315746).

Research Methods for Business and Social Science Students

Author: John Adams
Publisher: SAGE Publications India
ISBN: 8132119819
Format: PDF, ePub, Docs
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Research Methods for Business and Social Science Students aims to present a clear discussion of the research methods employed in various disciplines related to our daily life problems. The theoretical basis of research methods is explained clearly and succinctly. Collecting data is a key part of the book and this includes both qualitative and quantitative methods of data collection, along with the advantages and disadvantages of each method. The book also describes in clear terms how students can analyse data, interpret results and link these to the literature review and hence their own contribution. It sets out a range of fundamental ideas in research methods, such as deductivism and inductivism, and explains why methodology is not the same as method. In this second edition every chapter has been re-written to be more readable and also to include more examples. The authors have also added a real student research proposal and a multiple-choice test with answers for the readers to test their own understanding of the ideas in the book. The book has been designed to illustrate research tools in a clear and accessible manner through chapters on such topics as formulating research, research design, data analysis and writing up the research results.

Corpus Linguistics and Statistics with R

Author: Guillaume Desagulier
Publisher: Springer
ISBN: 3319645722
Format: PDF, ePub, Mobi
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This textbook examines empirical linguistics from a theoretical linguist’s perspective. It provides both a theoretical discussion of what quantitative corpus linguistics entails and detailed, hands-on, step-by-step instructions to implement the techniques in the field. The statistical methodology and R-based coding from this book teach readers the basic and then more advanced skills to work with large data sets in their linguistics research and studies. Massive data sets are now more than ever the basis for work that ranges from usage-based linguistics to the far reaches of applied linguistics. This book presents much of the methodology in a corpus-based approach. However, the corpus-based methods in this book are also essential components of recent developments in sociolinguistics, historical linguistics, computational linguistics, and psycholinguistics. Material from the book will also be appealing to researchers in digital humanities and the many non-linguistic fields that use textual data analysis and text-based sensorimetrics. Chapters cover topics including corpus processing, frequencing data, and clustering methods. Case studies illustrate each chapter with accompanying data sets, R code, and exercises for use by readers. This book may be used in advanced undergraduate courses, graduate courses, and self-study.